hansen.fit {ouch}R Documentation

Hansen model of evolution along a phylogenetic tree

Description

These functions relate to the Hansen model for phylogenetic evolution.

hansen.fit
fits the parameters alpha, sigma, and theta of this model.
hansen.dev
generates random deviates from the Hansen model.

Usage

hansen.fit(data, node, ancestor, times, regimes,
           guess = 0, interval = c(0.001, 20), tol = 1e-12)
hansen.dev(n = 1, node, ancestor, times, regimes, alpha, sigma, theta)

Arguments

data Phenotypic data for extant species, i.e., at the terminal ends of the phylogenetic tree.
node Character vector of names of the nodes.
ancestor Specification of the topology of the phylogenetic tree. This is in the form of a character vector of node names, one for each node in the tree. The i-th name is that of the ancestor of the i-th node. The root node is distinguished by having no ancestor (i.e., NA).
times A vector of nonnegative numbers, one per node in the tree, specifying the time at which each node is located. The root node should be assigned time 0.
regimes A vector of codes, one for each node in the tree, specifying the selective regimes hypothesized to have been operative. Corresponding to each node, enter the code of the regime hypothesized for the branch segment terminating in that node. For the root node, because it has no branch segment terminating on it, the regime specification is irrelevant.
guess (Optional). An initial guess for the selection strength parameter alpha. This is used to initialize the optimization algorithm.
interval The interval which will be searched for the optimal alpha. By default, 0.001 < alpha < 20.
tol Convergence tolerance.
n Number of pseudorandom data sets to generate.
alpha Value of alpha to use.
sigma Value of sigma to use.
theta Value of theta to use.

Value

hansen.fit returns a list containing the following elements:

alpha Maximum likelihood estimate of alpha. Note that if alpha lies against one of the constraints (see interval above), then this is not a maximum-likelihood estimate.
sigma Maximum likelihood estimate of sigma.
theta Maximum likelihood estimate of theta.
u -2 log likelihood.
aic Akaike information criterion.
sic Schwartz information criterion (=BIC)
df Number of parameters estimated (= 3 + number of regimes).


hansen.dev returns a data frame containing simulated data sets. Each realization is a row.

Author(s)

Aaron A. King <king at tiem dot utk dot edu>

References

Butler, M.A. and A.A. King (2004) Phylogenetic comparative analysis: a modeling approach for adaptive evolution. American Naturalist, in press.


[Package ouch version 1.1-1 Index]